• Korean Journal of Heritage: History & Science
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• v.43 no.3
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• pp.122-143
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• 2010

The Shilla Stone Monument in Jungseongri was found during the road-construction in Pohang. It has approximately two hundreds of letters inscribed on the surface of one side, and it is estimated to be older than Shilla Stone Monument in Naengsuri which had been known for the oldest stele in Shilla Period. This monument is made of fine to medium-grained biotite granite, while the Shilla Stone Monument in Naengsuri is made of fine-grained granodioritic porphyry bearing feldspar and amphibole phenocrysts. Both rock types of the monuments are interpreted to be cognate with biotite granite in Shinkwangmyeon, and with granodioritic porphyry in Gigyemyeon. They are characterized by xenolith and miarolitic cavity. Damage aspects in both monuments are discoloring, cracking and breaking. These damages do not cause structural instability of the monuments, but attenuate aesthetic value. Black and brown discoloring contaminants on the surface of the Jungseongri Monument contain a high amount of manganese and iron. As a result of ultrasonic test, both monuments were evaluated to be medium-weathered (MW), although the velocity of the Shilla Monument in Jungseongri was slightly lower than the Shilla Monument in Naengsuri. This is because the Monument in Juengseongri had been exposed to outdoor environment for long time until the discovery. It is necessary for Shilla Monuments to be protected by appropriately environmental control and management.

• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.3
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• pp.281-301
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• 2024

The power cable tunnels which are part of the underground transmission line project, are constructed using the shield TBM method. The disc cutter among the shield TBM components plays an important role in breaking rock mass. Efficient tunnel construction is possible only when appropriate replacement occurs as the wear limit is reached or damage such as uneven wear occurs. A study was conducted to determine the wear conditions of disc cutter using a deep learning algorithm based on real-time measurement data of wear and rotation speed. Based on the results of full-scaled tunnelling tests, it was confirmed that measurement data was obtained differently depending on the wear conditions of disc cutter. Using real-time measurement data, an algorithm was developed to determine disc cutter wear characteristics based on a convolutional neural network model. Distributional patterns of data can be learned through CNN filters, and the performance of the model that can classify uniform wear and uneven wear through these pattern features.